Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge comput...
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doaj-25d3ac474e064a2b8c81f0fe71c453782021-03-29T23:18:44ZengIEEEIEEE Access2169-35362019-01-01712398112399110.1109/ACCESS.2019.29382368819986Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing NetworksYupin Huang0Liping Qian1https://orcid.org/0000-0001-6210-2617Anqi Feng2Ningning Yu3Yuan Wu4College of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaDepartment of Computer and Information Science, University of Macau, Macau, ChinaReal-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.https://ieeexplore.ieee.org/document/8819986/Short-term traffic predictiondeep belief networkhidden Markov modeledge computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yupin Huang Liping Qian Anqi Feng Ningning Yu Yuan Wu |
spellingShingle |
Yupin Huang Liping Qian Anqi Feng Ningning Yu Yuan Wu Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks IEEE Access Short-term traffic prediction deep belief network hidden Markov model edge computing |
author_facet |
Yupin Huang Liping Qian Anqi Feng Ningning Yu Yuan Wu |
author_sort |
Yupin Huang |
title |
Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks |
title_short |
Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks |
title_full |
Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks |
title_fullStr |
Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks |
title_full_unstemmed |
Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks |
title_sort |
short-term traffic prediction by two-level data driven model in 5g-enabled edge computing networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models. |
topic |
Short-term traffic prediction deep belief network hidden Markov model edge computing |
url |
https://ieeexplore.ieee.org/document/8819986/ |
work_keys_str_mv |
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1724189771464441856 |